code: def __iter__(self):
      buffer = []
      for df in self.source_datapipe:
          buffer.append(df)
          if len(buffer) == self.n_batch:
              yield df_wrapper.concat(buffer)
              buffer = []
      if len(buffer):
          yield df_wrapper.concat(buffer)
  
imports: import random
  from torch.utils.data.datapipes._decorator import functional_datapipe
  from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
  from torch.utils.data.datapipes.dataframe import dataframe_wrapper
label_desc: Use image node_id 2 for calling the ConcatDataFramesPipe obj's underlying member method code with example usage: obj.__iter__() without return types